A fault diagnosis method based on wavelet energy spectrum and support vector machine(SVM)was proposed in terms of less fault samples,weakness of signals and difficulty to be extracted of wind power slewing bearing.Feature vectors were constructed by combining wavelet energy spectrum of acceleration signal with temperature and torque signal.And three normal states: single bolt fracture and multiple bolt fracture were classified by using SVM with classification accuracy of 100%,while the classification accuracies for the same samples reached only 84%,92%,and 80%, respectively by the BP neural network method.Results show that SVM method is more suitable than BP neural network method for wind power slewing bearing fault diagnosis.